Search (207 results, page 1 of 11)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.14 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
    Pages
    S.331-334
  2. Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999) 0.03 
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    Pages
    S.239-246
  3. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.03 
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    Date
    5. 5.2003 14:17:22
    Source
    Journal of library administration. 34(2001) nos.3/4, S.221-228
  4. Liu, R.-L.: Context-based term frequency assessment for text classification (2010) 0.02 
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    Abstract
    Automatic text classification (TC) is essential for the management of information. To properly classify a document d, it is essential to identify the semantics of each term t in d, while the semantics heavily depend on context (neighboring terms) of t in d. Therefore, we present a technique CTFA (Context-based Term Frequency Assessment) that improves text classifiers by considering term contexts in test documents. The results of the term context recognition are used to assess term frequencies of terms, and hence CTFA may easily work with various kinds of text classifiers that base their TC decisions on term frequencies, without needing to modify the classifiers. Moreover, CTFA is efficient, and neither huge memory nor domain-specific knowledge is required. Empirical results show that CTFA successfully enhances performance of several kinds of text classifiers on different experimental data.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.300-309
  5. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
    Pages
    S.64-75
  6. Koch, T.: Nutzung von Klassifikationssystemen zur verbesserten Beschreibung, Organisation und Suche von Internetressourcen (1998) 0.02 
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    Source
    BuB. 50(1998) H.5, S.326-335
  7. Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.02 
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    Source
    c't. 2003, H.19, S.192-197
  8. Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006) 0.02 
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    Pages
    S.129-146
    Source
    Effektive Information Retrieval Verfahren in Theorie und Praxis: ausgewählte und erweiterte Beiträge des Vierten Hildesheimer Evaluierungs- und Retrievalworkshop (HIER 2005), Hildesheim, 20.7.2005. Hrsg.: T. Mandl u. C. Womser-Hacker
  9. Jersek, T.: Automatische DDC-Klassifizierung mit Lingo : Vorgehensweise und Ergebnisse (2012) 0.02 
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    Pages
    V. 56 S
  10. Koch, T.: Experiments with automatic classification of WAIS databases and indexing of WWW : some results from the Nordic WAIS/WWW project (1994) 0.02 
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    Pages
    S.112-115
  11. Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.02 
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    Source
    Journal of intelligent information systems. 29(2007) no.2, S.211-230
  12. Golub, K.; Hamon, T.; Ardö, A.: Automated classification of textual documents based on a controlled vocabulary in engineering (2007) 0.02 
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    Source
    Knowledge organization. 34(2007) no.4, S.247-263
  13. Leroy, G.; Miller, T.; Rosemblat, G.; Browne, A.: ¬A balanced approach to health information evaluation : a vocabulary-based naïve Bayes classifier and readability formulas (2008) 0.02 
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    Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1409-1419
  14. Cosh, K.J.; Burns, R.; Daniel, T.: Content clouds : classifying content in Web 2.0 (2008) 0.02 
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    Source
    Library review. 57(2008) no.9, S.722-729
  15. Malenica, M.; Smuc, T.; Snajder, J.; Basic, B.D.: Language morphology offset : text classification on a Croatian-English parallel corpus (2008) 0.02 
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    Information processing and management. 44(2008) no.1, S.325-339
  16. Montesi, M.; Navarrete, T.: Classifying web genres in context : A case study documenting the web genres used by a software engineer (2008) 0.02 
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    Information processing and management. 44(2008) no.4, S.1410-1430
  17. Schaalje, G.B.; Blades, N.J.; Funai, T.: ¬An open-set size-adjusted Bayesian classifier for authorship attribution (2013) 0.02 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1815-1825
  18. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.02 
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  19. Dubin, D.: Dimensions and discriminability (1998) 0.02 
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    Date
    22. 9.1997 19:16:05
    Pages
    S.39-44
  20. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.02 
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.646-648

Authors

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  • e 163
  • d 42
  • chi 1
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Types

  • a 178
  • el 21
  • x 9
  • m 4
  • r 4
  • s 2
  • d 1
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